{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "02_Keras_RNN_TPU_temperatures_playground.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hzqv4_PYfqre",
        "colab_type": "text"
      },
      "source": [
        "# An RNN model for temperature data\n",
        "This time we will be working with real data: daily (Tmin, Tmax) temperature series from 1666 weather stations spanning 50 years. It is to be noted that a pretty good predictor model already exists for temperatures: the average of temperatures on the same day of the year in N previous years. It is not clear if RNNs can do better but we will se how far they can go."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tF-Aljv4fqrg",
        "colab_type": "code",
        "outputId": "94598193-0cac-42fe-cec6-7caac45d18c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os, re, math\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "from matplotlib import transforms as plttrans\n",
        "#%tensorflow_version 2.x # not yet supported on TPU (it will be in TF 2.1)\n",
        "import tensorflow as tf\n",
        "print(\"Tensorflow version: \" + tf.__version__)\n",
        "\n",
        "MAX_DATE = 18262 # 50 years of data, Tmin, Tmax each day"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensorflow version: 1.15.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AB8zBNHBVJqL",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Data formatting and display utilites [RUN ME]\n",
        "\n",
        "# Training data sequencer: this version works from TFRecords and uses tf.data.Dataset\n",
        "def rnn_dataset_sequencer3tfrec(filenames, resample_by=1, batch_size=9999, seqlen=MAX_DATE, n_forward=0, nb_epochs=1, tminmax_only=False, drop_remainder=False, ordered=False):\n",
        "  # drop_remainder applies to batch size only,\n",
        "  # the remainders introduced by \"resample_by\" and \"seqlen\" are always dropped\n",
        "  \n",
        "  def truncate(x, n):\n",
        "    return x[:x.get_shape()[0] // n * n]\n",
        "  \n",
        "  def read_tfrecord(example):\n",
        "      feature = {\n",
        "        \"date\": tf.io.FixedLenFeature([MAX_DATE], tf.string),\n",
        "        \"tmin\": tf.io.FixedLenFeature([MAX_DATE], tf.float32),\n",
        "        \"tmax\": tf.io.FixedLenFeature([MAX_DATE], tf.float32),\n",
        "        \"interpolated\": tf.io.FixedLenFeature([MAX_DATE], tf.int64)\n",
        "      }\n",
        "      example = tf.io.parse_single_example(example, feature)\n",
        "      temps = tf.stack([example['tmin'], example['tmax']], axis=1)\n",
        "      return temps, example['date'], example['interpolated']\n",
        "\n",
        "  def truncate(x, n):\n",
        "    return x[:x.get_shape()[0] // n * n]\n",
        "  \n",
        "  def process_metadata(x, mean=False):\n",
        "    x = truncate(x, resample_by)\n",
        "    x = tf.reshape(x, [-1, resample_by])\n",
        "    if mean:\n",
        "      x = tf.math.greater(tf.reduce_mean(tf.cast(x, tf.float32), axis=1), 0.0)\n",
        "    else:\n",
        "      x = x[:,0]\n",
        "    x = x[:x.get_shape()[0]-n_forward] # allows n_forward=0\n",
        "    x = truncate(x, seqlen)\n",
        "    x = tf.reshape(x, [-1, seqlen])\n",
        "    return x\n",
        "  \n",
        "  def process_data(temps, dates, interpolated):\n",
        "    temps = truncate(temps, resample_by)\n",
        "    temps = tf.reduce_mean(tf.reshape(temps, [-1, resample_by, 2]), axis=1)\n",
        "    temps, targets = temps[:temps.get_shape()[0]-n_forward], temps[n_forward:] # allows n_forward=0\n",
        "    temps, targets = truncate(temps, seqlen), truncate(targets, seqlen)\n",
        "    temps, targets = tf.reshape(temps, [-1, seqlen, 2]), tf.reshape(targets, [-1, seqlen, 2])\n",
        "    return temps, targets, process_metadata(dates), process_metadata(interpolated, mean=True)\n",
        "  \n",
        "  dataset = tf.data.TFRecordDataset(filenames, \"GZIP\", num_parallel_reads=1 if ordered else 16) # 16 or 32 same perf\n",
        "  dataset = dataset.map(read_tfrecord, num_parallel_calls=32)\n",
        "  dataset = dataset.map(process_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "  dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)\n",
        "  dataset = dataset.map(lambda x, y, z, t: (tf.transpose(x, [1, 0, 2, 3]), tf.transpose(y, [1, 0, 2, 3]),  tf.transpose(z, [1, 0, 2]),  tf.transpose(t, [1, 0, 2])))\n",
        "  dataset = dataset.unbatch()\n",
        "  \n",
        "  if tminmax_only:\n",
        "    dataset = dataset.map(lambda temps, targets, date, interp: (temps, targets))\n",
        "    \n",
        "  dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n",
        "  dataset = dataset.cache()\n",
        "  dataset = dataset.repeat(nb_epochs)\n",
        "  return dataset\n",
        "\n",
        "\n",
        "# override default matplotlib styles\n",
        "plt.rcParams['figure.figsize']=(16.8,6.0)\n",
        "plt.rcParams['axes.grid']=True\n",
        "plt.rcParams['axes.linewidth']=0\n",
        "plt.rcParams['grid.color']='#DDDDDD'\n",
        "plt.rcParams['axes.facecolor']='white'\n",
        "plt.rcParams['xtick.major.size']=0\n",
        "plt.rcParams['ytick.major.size']=0\n",
        "plt.rcParams['axes.titlesize']=15.0\n",
        "\n",
        "def display_lr(lr_schedule, nb_epochs):\n",
        "  x = np.arange(nb_epochs)\n",
        "  y = [lr_schedule(i) for i in x]\n",
        "  plt.figure(figsize=(9,5))\n",
        "  plt.step(x,y, where='post')\n",
        "  plt.title(\"Learning rate schedule\\nmax={:.2e}, min={:.2e}\".format(np.max(y), np.min(y)),\n",
        "            y=0.85)\n",
        "  plt.xlabel('EPOCH')\n",
        "  plt.show()\n",
        "  \n",
        "def display_loss(history, full_history, nb_epochs):\n",
        "  plt.figure()\n",
        "  plt.plot(np.arange(0, len(full_history['loss']))/steps_per_epoch, full_history['loss'], label='detailed loss')\n",
        "  plt.plot(np.arange(1, nb_epochs+1), history['loss'], color='red', linewidth=3, label='average loss per epoch')\n",
        "  plt.ylim(0,3*max(history['loss'][1:]))\n",
        "  plt.xlabel('EPOCH')\n",
        "  plt.ylabel('LOSS')\n",
        "  plt.xlim(0, nb_epochs+0.5)\n",
        "  plt.legend()\n",
        "  for epoch in range(nb_epochs//2+1):\n",
        "    plt.gca().axvspan(2*epoch, 2*epoch+1, alpha=0.05, color='grey')\n",
        "  plt.show()\n",
        "\n",
        "def dataset_to_numpy(dataset, batches=1):\n",
        "  data = []\n",
        "  \n",
        "  # In eager mode, iterate on the Datset directly.\n",
        "  if tf.executing_eagerly():\n",
        "    for i, data_batch in zip(range(batches), dataset): # will stop on shortest\n",
        "      data.append(data_batch)\n",
        "\n",
        "      # parse dates\n",
        "    data = [(samples.numpy(),\n",
        "            targets.numpy(),\n",
        "            np.array(dates.numpy(), dtype='datetime64'),\n",
        "            interpolated.numpy())\n",
        "            for samples, targets, dates, interpolated in data]\n",
        "\n",
        "  # In non-eager mode, must get the TF node that\n",
        "  # yields the next item and run it in a tf.Session.\n",
        "  else:\n",
        "    get_next_item = tf.compat.v1.data.make_one_shot_iterator(dataset).get_next()\n",
        "    with tf.Session() as ses:\n",
        "      for _ in range(batches):\n",
        "        data.append(ses.run(get_next_item))\n",
        "      \n",
        "    # parse dates\n",
        "    data = [(samples,\n",
        "            targets,\n",
        "            np.array(dates, dtype='datetime64'),\n",
        "            interpolated)\n",
        "            for samples, targets, dates, interpolated in data]\n",
        "\n",
        "  return data\n",
        "\n",
        "        \n",
        "def picture_this_4(temperatures, dates, interpolated):\n",
        "  min_temps = temperatures[:,0]\n",
        "  max_temps = temperatures[:,1]\n",
        "  #interpolated = temperatures[:,2]\n",
        "\n",
        "  interpolated_sequence = False\n",
        "  #plt.plot(dates, max_temps)\n",
        "  for i, date in enumerate(dates):\n",
        "    if interpolated[i]:\n",
        "      if not interpolated_sequence:\n",
        "        startdate = date\n",
        "      interpolated_sequence = True\n",
        "      stopdate = date\n",
        "    else:\n",
        "      if interpolated_sequence:\n",
        "        # light shade of red just for visibility\n",
        "        plt.axvspan(startdate+np.timedelta64(-5, 'D'), stopdate+np.timedelta64(6, 'D'), facecolor='#FFCCCC', alpha=1)\n",
        "        # actual interpolated region\n",
        "        plt.axvspan(startdate+np.timedelta64(-1, 'D'), stopdate+np.timedelta64(1, 'D'), facecolor='#FF8888', alpha=1)\n",
        "      interpolated_sequence = False\n",
        "  plt.fill_between(dates, min_temps, max_temps).set_zorder(10)\n",
        "  plt.show()\n",
        "  \n",
        "def picture_this_5(visu_data, station):\n",
        "  subplot = 231\n",
        "  for samples, targets, dates, _ in visu_data:\n",
        "    plt.subplot(subplot)\n",
        "    h1 = plt.fill_between(dates[station], samples[station,:,0], samples[station,:,1], label=\"features\")\n",
        "    h2 = plt.fill_between(dates[station], targets[station,:,0], targets[station,:,1], label=\"labels\")\n",
        "    h2.set_zorder(-1)\n",
        "    if subplot == 231:\n",
        "      plt.legend(handles=[h1, h2])\n",
        "    subplot += 1\n",
        "    if subplot==237:\n",
        "      break\n",
        "  plt.show()\n",
        "  \n",
        "def picture_this_6(evaldata, evaldates, prime_data, results, primelen, runlen, offset, rmselen):\n",
        "  disp_data = evaldata[offset:offset+primelen+runlen]\n",
        "  disp_dates = evaldates[offset:offset+primelen+runlen]\n",
        "  colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
        "  displayresults = np.ma.array(np.concatenate((np.zeros([primelen,2]), results)))\n",
        "  displayresults = np.ma.masked_where(displayresults == 0, displayresults)\n",
        "  sp = plt.subplot(212)\n",
        "  p = plt.fill_between(disp_dates, displayresults[:,0], displayresults[:,1])\n",
        "  p.set_alpha(0.8)\n",
        "  p.set_zorder(10)\n",
        "  trans = plttrans.blended_transform_factory(sp.transData, sp.transAxes)\n",
        "  plt.text(disp_dates[primelen],0.05,\"DATA |\", color=colors[1], horizontalalignment=\"right\", transform=trans)\n",
        "  plt.text(disp_dates[primelen],0.05,\"| +PREDICTED\", color=colors[0], horizontalalignment=\"left\", transform=trans)\n",
        "  plt.fill_between(disp_dates, disp_data[:,0], disp_data[:,1])\n",
        "  plt.axvspan(disp_dates[primelen], disp_dates[primelen+rmselen], color='grey', alpha=0.1, ymin=0.05, ymax=0.95)\n",
        "  plt.show()\n",
        "\n",
        "  rmse = math.sqrt(np.mean((evaldata[offset+primelen:offset+primelen+rmselen] - results[:rmselen])**2))\n",
        "  print(\"RMSE on {} predictions (shaded area): {}\".format(rmselen, rmse))\n",
        "\n",
        "def display_training_curves(training, validation, title, subplot):\n",
        "  if subplot%10==1: # set up the subplots on the first call\n",
        "    plt.subplots(figsize=(10,10), facecolor='#F0F0F0')\n",
        "    plt.tight_layout()\n",
        "  ax = plt.subplot(subplot)\n",
        "  ax.set_facecolor('#F8F8F8')\n",
        "  ax.plot(training)\n",
        "  ax.plot(validation)\n",
        "  ax.set_title('model '+ title)\n",
        "  ax.set_ylabel(title)\n",
        "  ax.set_xlabel('epoch')\n",
        "  ax.legend(['train', 'valid.'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPo10cahZXXQ",
        "colab_type": "text"
      },
      "source": [
        "## TPU detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FpvUOuC3j27n",
        "colab_type": "code",
        "outputId": "fd3995cd-1d9a-479e-a9e9-82803537621e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "try: # TPU detection\n",
        "  tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n",
        "except ValueError:\n",
        "  tpu = None\n",
        "\n",
        "if tpu:\n",
        "  tf.config.experimental_connect_to_cluster(tpu)\n",
        "  tf.tpu.experimental.initialize_tpu_system(tpu)\n",
        "  strategy = tf.distribute.experimental.TPUStrategy(tpu)\n",
        "  print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])\n",
        "else:\n",
        "  strategy = tf.distribute.get_strategy()\n",
        "  print(\"Running on GPU or CPU\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: 10.13.65.178:8470\n",
            "INFO:tensorflow:Finished initializing TPU system.\n",
            "INFO:tensorflow:Querying Tensorflow master (grpc://10.13.65.178:8470) for TPU system metadata.\n",
            "INFO:tensorflow:Found TPU system:\n",
            "INFO:tensorflow:*** Num TPU Cores: 8\n",
            "INFO:tensorflow:*** Num TPU Workers: 1\n",
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 5564860144403918059)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 15456049584327680502)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 1803024835142058145)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 7551287676501649088)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 129894155122236273)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 17568589077660702180)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 2171507076967841809)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 8355328581219176135)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 8814030853360033778)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 8589934592, 7246505852695611542)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 9816714486401413587)\n",
            "Running on TPU  ['10.13.65.178:8470']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qs4tak_hfqrm",
        "colab_type": "text"
      },
      "source": [
        "## Hyperparameters\n",
        "N_FORWARD = 1: works but model struggles to predict from some positions<br/>\n",
        "N_FORWARD = 4: better but still bad occasionnally<br/>\n",
        "N_FORWARD = 8: works perfectly "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qv1s67Olfqrm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "N_FORWARD = 1       # train the network to predict N in advance (traditionnally 1)\n",
        "RESAMPLE_BY = 1     # averaging period in days (training on daily data is too much)\n",
        "RNN_CELLSIZE = 128  # size of the RNN cells\n",
        "SEQLEN = 166        # unrolled sequence length\n",
        "BATCHSIZE = 256     # mini-batch size\n",
        "DROPOUT = 0.3       # dropout regularization: probability of neurons being dropped. Should be between 0 and 0.5\n",
        "\n",
        "# If you are accessing private GCS buckets from Colab, you must\n",
        "# authenticate. No need here as the data bucket is public.\n",
        "#\n",
        "# from google.colab import auth\n",
        "# auth.authenticate_user()\n",
        "\n",
        "DATA_DIR = 'gs://good-temperatures-public/'\n",
        "DATA_DIR_TFREC = 'gs://good-temperatures-public-tfrecords/'\n",
        "ALL_FILEPATTERN = DATA_DIR_TFREC + 'temperatures_*.tfrec'\n",
        "EVAL_FILEPATTERN = DATA_DIR_TFREC + 'temperatures_*_nb_?.tfrec'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZAHTvGTfqrp",
        "colab_type": "text"
      },
      "source": [
        "## Temperature data\n",
        "This is what our temperature datasets looks like: sequences of daily (Tmin, Tmax) from 1960 to 2010. They have been cleaned up and eventual missing values have been filled by interpolation. Interpolated regions of the dataset are marked in red on the graph."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tIYIGunzfqrp",
        "colab_type": "code",
        "outputId": "521c8dc7-8abf-4bc3-be92-18621b2a47ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "all_filenames = tf.gfile.Glob(ALL_FILEPATTERN)\n",
        "eval_filenames = tf.gfile.Glob(EVAL_FILEPATTERN)\n",
        "train_filenames = list(set(all_filenames) - set(eval_filenames))\n",
        "\n",
        "# TFRecord files contain data for multiple weather stations each. The number is in the file name.\n",
        "nb_finder = re.compile(r'nb_([0-9]*)\\.')  # nb of stations in 'temperatures_weather_stations_0000_to_0003_nb_4.tfrec'\n",
        "nb_stations = sum([int(nb_finder.search(s).group(1)) for s in all_filenames])\n",
        "nb_eval_stations = sum([int(nb_finder.search(s).group(1)) for s in eval_filenames])\n",
        "nb_train_stations = sum([int(nb_finder.search(s).group(1)) for s in train_filenames])\n",
        "\n",
        "print('Pattern \"{}\" matches {} files containing {} weather stations'.format(ALL_FILEPATTERN, len(all_filenames), nb_stations))\n",
        "print('Eval pattern \"{}\" matches {} evaluation files containing {} weather stations'.format(EVAL_FILEPATTERN, len(eval_filenames), nb_eval_stations))\n",
        "print('The remaining {} training files contain {} weather stations'.format(len(train_filenames), nb_train_stations))\n",
        "\n",
        "# By default, this utility function loads all the weather stations and places\n",
        "# data from them as-is in an array, one station per line. Later, we will use\n",
        "# it to shape the dataset as needed for training.\n",
        "\n",
        "eval_dataset = rnn_dataset_sequencer3tfrec(eval_filenames)\n",
        "evtemps, evtargets, evdates, evinter = dataset_to_numpy(eval_dataset)[0]\n",
        "\n",
        "print()\n",
        "print(\"Initial shape of the evaluation dataset: \" + str(evtemps.shape))\n",
        "print(\"{} weather stations, {} data points per station (50 years), {} values per data point (Tmin, Tmax) \".format(evtemps.shape[0], evtemps.shape[1],evtemps.shape[2]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Pattern \"gs://good-temperatures-public-tfrecords/temperatures_*.tfrec\" matches 30 files containing 1666 weather stations\n",
            "Eval pattern \"gs://good-temperatures-public-tfrecords/temperatures_*_nb_?.tfrec\" matches 3 evaluation files containing 18 weather stations\n",
            "The remaining 27 training files contain 1648 weather stations\n",
            "\n",
            "Initial shape of the evaluation dataset: (18, 18262, 2)\n",
            "18 weather stations, 18262 data points per station (50 years), 2 values per data point (Tmin, Tmax) \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [
          "display"
        ],
        "id": "AwaXhjN-fqrs",
        "colab_type": "code",
        "outputId": "84955781-3141-43a3-d8ba-587137c8f6aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "# You can adjust the visualisation range and dataset here.\n",
        "# Interpolated regions of the dataset are marked in red.\n",
        "WEATHER_STATION = 0 # 0 to 7 in default eval dataset\n",
        "START_DATE = 0      # 0 = Jan 2nd 1950\n",
        "END_DATE = MAX_DATE # 18262 = Dec 31st 2009\n",
        "visu_temperatures = evtemps[WEATHER_STATION, START_DATE:END_DATE]\n",
        "visu_dates        = evdates[WEATHER_STATION, START_DATE:END_DATE]\n",
        "visu_interpolated = evinter[WEATHER_STATION, START_DATE:END_DATE]\n",
        "\n",
        "picture_this_4(visu_temperatures, visu_dates, visu_interpolated)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IkyC2tZXKHse+P/IbuV89DoFb6HNqAfU1A6peGh8Y/HWUqTN0aWAfYq3NrdG2vm6kVoiI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BwzDh4TRkSjaQh2hwv+j4aijlTs8Fk82RttETzVJDWhjT+5/Z08v4OWtjoroYdrFqxfNYTfBI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43nQjB6jel7DnhG459t58vHWwDI7okSzZ3zWBs377+dUQzteexMVEY5n62JprLyOBVTpn6k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\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0iFQRVgaHCHC6CvCJgt46BwYwraUjkWVUmBgWnIxhp9RBC54tpY7eNgb3D0uJQe5/XRn7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\nMzMzMzNL7iSbmZmZmZmZpX8BgZ9RMT5lw28AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-3jMA6Rfqrv",
        "colab_type": "text"
      },
      "source": [
        "## Resampling\n",
        "In this playground notebook, resampling is initially set to RESAMPLE_BY=1 (no resampling).\n",
        "This is what (Tmin, Tmax) temperatures look like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mBySbBYfqrv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# This time we ask the utility function to average temperatures over RESAMPLE_BY day periods (in days)\n",
        "eval_dataset = rnn_dataset_sequencer3tfrec(eval_filenames, RESAMPLE_BY, seqlen=MAX_DATE//RESAMPLE_BY)\n",
        "evaluation_data = dataset_to_numpy(eval_dataset)[0] # all data returned as one batch\n",
        "evaltemps, _, evaldates, _ = evaluation_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [
          "display"
        ],
        "id": "jIGbTsUXfqrz",
        "colab_type": "code",
        "outputId": "a2903df7-8f48-46d0-d5ec-391074b37f6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "# display five years worth of data\n",
        "WEATHER_STATION = 0              # 0 to 7 in default eval dataset\n",
        "START_DATE = 0                   # 0 = Jan 2nd 1950\n",
        "END_DATE = 365*5//RESAMPLE_BY    # 5 years\n",
        "visu_temperatures = evaltemps[WEATHER_STATION, START_DATE:END_DATE]\n",
        "visu_dates        = evaldates[WEATHER_STATION, START_DATE:END_DATE]\n",
        "plt.fill_between(visu_dates, visu_temperatures[:,0], visu_temperatures[:,1])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VCeqEK/vfTKhga1cT+46ZFiaDmNSV1oWocKweJwK2xwlc352CUMYaMGZCVyrP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SxRiQwI2uFVwKXir1f+L8UP07/fzK7pc6/WongZzcms5J5ktHynkEGsUh6ht3qc/26ctj8r2s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\nSMr/B4yeI39XqWyrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgrgR0sTfqr2",
        "colab_type": "text"
      },
      "source": [
        "## Visualize training sequences\n",
        "This is what the neural network will see during training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [
          "display"
        ],
        "id": "nJe3ysbZfqr2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The sequencing function puts one weather station per line in a batch and\n",
        "# continues with data from the same station in corresponding lines in the next batch.\n",
        "# Features and labels are returned with shapes [BATCHSIZE, SEQLEN, 2]\n",
        "\n",
        "eval_dataset = rnn_dataset_sequencer3tfrec(eval_filenames,\n",
        "                                           RESAMPLE_BY,\n",
        "                                           BATCHSIZE,\n",
        "                                           SEQLEN,\n",
        "                                           N_FORWARD)\n",
        "\n",
        "visu_data = dataset_to_numpy(eval_dataset, batches=6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [
          "display"
        ],
        "id": "Ea90ux30fqr5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Check that consecutive training sequences from the same weather station are indeed consecutive\n",
        "WEATHER_STATION = 0\n",
        "picture_this_5(visu_data, WEATHER_STATION)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGVfLW7DAyTs",
        "colab_type": "text"
      },
      "source": [
        "## Keras model [WORK REQUIRED]\n",
        "1. This model needs even more compute power. Let's use TPU (Tensor Processing Unit) acceleration.<br/>\n",
        "Go to Runtime > Runtime Type and check that \"TPU\" is selected.<br/>\n",
        "For information, training times for 10 epochs on the final model are:<br/>\n",
        " * CPU: 40 min<br/>\n",
        " * GPU: 11 min<br/>\n",
        " * GPU using optimized CuDNNGRU cells: 2 min<br/>\n",
        " * TPU: 35 s<br/>\n",
        " You can check these times later using the solution notebook.\n",
        "1. Run the notebook as it is with the dummy model. Check the temperature predictions in the Validation section. They are really bad of course.\n",
        "1. Now implement a 2-layer RNN using stateful GRU cells.\n",
        " * The input and output shapes are both [batchsize, seqlen, 2]. The 2 is for (Tmin, Tmax). The model will be trained to output the same sequence shifted by one i.e. to predict the next value after the input sequence.\n",
        " * Do not forget to unroll the readout regression layer with tf.ketas.layers.TimeDistributed()\n",
        " * Check your shapes, yada yada yada <font size=\"+2\">🤯</font>you know the drill now.\n",
        "1. Try to train it using all 1666 weather stations for 4 epochs\n",
        " * The final RMSE is promising but the loss is really noisy and the predictions are horrible (and slow, stop after a couple)\n",
        "1. The model is good, something is wrong with the data. Look at the training sequences in the cell above. Think. You are a data scientist, you should be able to figure it out.\n",
        "1. Daily temperatures fluctuate too much. Let's try to predict 5-day averages instead.\n",
        " * Set RESAMPLE_BY=5 in section \"Hyperparameters\"\n",
        " * Set NB_EPOCHS=8 in section \"The training loop\"\n",
        " * Re-train from scratch (menu Runtime > \"Reset all runtimes\" to reset everything).<br/>\n",
        "  => Notice in section \"Visualize training sequences\" that a training sequence now spans multiple years. This should make it easier for the RNN to spot a pattern.<br/>\n",
        "  => Look at predictions. They are starting to move but are still off.\n",
        "1. Look at the data visualization above. The average temperature of the next 5 days is still very random. There is a trend but on a longer time frame. The trick is to train the RNN to predict the next 8 values instead of the next 1. This can be achieved by shifting the data by 8 instead of 1 when creating the targets. 8 five-day periods ahead, the temperature trend should be quite clear.\n",
        " * Set N_FORWARD=8 in section \"Hyperparameters\"\n",
        " * Set NB_EPOCHS=10 in section \"The training loop\"\n",
        " * Re-train from scratch (menu Runtime > \"Reset all runtimes\" to reset everything)<br/>\n",
        "![alt text](https://googlecloudplatform.github.io/tensorflow-without-a-phd/docs/images/RNN2.svg)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtSxFG2uA0Xs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def keras_model(batchsize, seqlen, stateful):\n",
        "  \n",
        "  model = tf.keras.Sequential([\n",
        "      # Instead of specifyting the input_shape and batch size on the first layer, you can have an InputLayer to hold these parameters.\n",
        "      tf.keras.layers.InputLayer(batch_size=batchsize, input_shape=[seqlen, 2]), # 2 for (Tmin, Tmax). Batch size is mandatory in Keras stateful RNN.\n",
        "\n",
        "      #\n",
        "      # YOUR LAYERS HERE\n",
        "      # do not forget to pass the \"stateful\" boolean to your RNN layers\n",
        "      #\n",
        "      tf.keras.layers.Lambda(lambda x: x) # dummy model\n",
        "  ]) \n",
        "  \n",
        "  model.compile(\n",
        "      optimizer='rmsprop',\n",
        "      loss='mean_squared_error',\n",
        "      metrics=['RootMeanSquaredError'])\n",
        "\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKoaFU4dizWw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Keras model callbacks\n",
        "\n",
        "# This callback records a per-step loss history instead of the average loss per\n",
        "# epoch that Keras normally reports. It allows you to see more problems.\n",
        "class LossHistory(tf.keras.callbacks.Callback):\n",
        "  def on_train_begin(self, logs={}):\n",
        "      self.history = {'loss': []}\n",
        "  def on_batch_end(self, batch, logs={}):\n",
        "      self.history['loss'].append(logs.get('loss'))\n",
        "\n",
        "# Learning Rate decay absolutely necessary on this model\n",
        "def lr_schedule(epoch): return 0.0001 + 0.01 * math.pow(0.6, epoch)\n",
        "lr_decay = tf.keras.callbacks.LearningRateScheduler(lr_schedule, verbose=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84yTcBFeV0XO",
        "colab_type": "text"
      },
      "source": [
        "## The training loop\n",
        "\n",
        "Training a stateful RNN theoretically requires data from weather stations to be arranged into batches so that data from one weather station continues \n",
        "on the same line across all batches. This way, the RNN state resulting from one batch is the correct RNN state for the next batch. In practice, training with bad input states introduces noise at the begining of each sequence, but with sequences long enough, the impact can be small. Most data scientists do not bother with correct sequence ordering because of the complexity. You can have a look at the code of the rnn_dataset_sequencer3tfrec function in the \"utilities\" section. It does the batch sequencing correctly for stateful RNNs but it is a bit complicated.\n",
        "![alt text](https://googlecloudplatform.github.io/tensorflow-without-a-phd/docs/images/batching.svg)\n",
        "\n",
        "On top of that, on TPUs, stateful RNNs are not (yet) supported."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXfiK0WvKVkg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "NB_EPOCHS = 5 # number of times the model sees all the data during training\n",
        "\n",
        "# The model\n",
        "with strategy.scope():\n",
        "  model = keras_model(BATCHSIZE, SEQLEN, stateful=False) # stateful RNNs not (yet) supported on TPUs\n",
        "\n",
        "# this prints a description of the model\n",
        "model.summary()\n",
        "\n",
        "display_lr(lr_schedule, NB_EPOCHS)\n",
        "\n",
        "# The dataset\n",
        "dataset = rnn_dataset_sequencer3tfrec(train_filenames,\n",
        "                                      RESAMPLE_BY,\n",
        "                                      BATCHSIZE,\n",
        "                                      SEQLEN,\n",
        "                                      N_FORWARD,\n",
        "                                      NB_EPOCHS,\n",
        "                                      tminmax_only=True,\n",
        "                                      drop_remainder=True) # this is needed on GPU (bug?)\n",
        "  \n",
        "# Training\n",
        "steps_per_epoch = (nb_train_stations//BATCHSIZE) * (((MAX_DATE//RESAMPLE_BY)-(N_FORWARD))//SEQLEN)\n",
        "full_history = LossHistory()\n",
        "history = model.fit(dataset,\n",
        "                    steps_per_epoch=steps_per_epoch,\n",
        "                    epochs=NB_EPOCHS,\n",
        "                    callbacks=[lr_decay, full_history])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vA8LJ253NLA3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "display_loss(history.history, full_history.history, NB_EPOCHS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9IEc_6Yt7IBw",
        "colab_type": "text"
      },
      "source": [
        "## Inference model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9lvIzqHUvaz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inference_model = keras_model(1, N_FORWARD, stateful=True) # here, the stateful RNN is required\n",
        "inference_model.set_weights(model.get_weights()) # copy weights from TPU to CPU (inference will be CPU-based)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vdDTSBqabYp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Inference from stateful model\n",
        "def keras_prediction_run(model, prime_data, run_length):\n",
        "  model.reset_states()\n",
        "  \n",
        "  data_len = prime_data.shape[0]\n",
        "  \n",
        "  Yin = prime_data[-(data_len//N_FORWARD)*N_FORWARD:]  # trim start of sequence to make length divisible by N_FORWARD\n",
        "  Yin = np.reshape(Yin, [data_len//N_FORWARD, N_FORWARD, 2])\n",
        "  \n",
        "  # prime the state from data\n",
        "  if data_len//N_FORWARD > 0:\n",
        "    for i in range(data_len//N_FORWARD - 1): # keep last N_FORWARD samples to serve as the input sequence for predictions\n",
        "      model.predict(np.expand_dims(Yin[i], axis=0))\n",
        "\n",
        "    # prime predictions with real data: the last N_FORWARD samples\n",
        "    Yout = np.expand_dims(Yin[data_len//N_FORWARD - 1], axis=0)\n",
        "    # Yout shape [1, N_FORWARD, 2]: batch of a single sequence of length N_FORWARD of (Tmin, Tmax) data pointa\n",
        "  \n",
        "  # prediction run\n",
        "  results = []\n",
        "  for i in range(run_length//N_FORWARD+1):\n",
        "    Yout = model.predict(Yout)\n",
        "    results.append(Yout[0]) # shape [N_FORWARD, 2]\n",
        "\n",
        "  return np.concatenate(results, axis=0)[:run_length]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h28kY6p8fqsO",
        "colab_type": "text"
      },
      "source": [
        "## Visual validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPiimpJ6fqsO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "QYEAR = 365//(RESAMPLE_BY*4)\n",
        "YEAR = 365//(RESAMPLE_BY)\n",
        "\n",
        "# Try starting predictions from January / March / July (resp. OFFSET = YEAR or YEAR+QYEAR or YEAR+2*QYEAR)\n",
        "# Some start dates are more challenging for the model than others.\n",
        "OFFSET = 30*YEAR+1*QYEAR\n",
        "PRIMELEN=5*YEAR\n",
        "RUNLEN=3*YEAR\n",
        "RMSELEN=3*365//(RESAMPLE_BY*2) # accuracy of predictions 1.5 years in advance"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [
          "display"
        ],
        "id": "ATfiiWMjfqsT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "selected_evaluation_data = tuple(data[np.array([1, 2, 3, 4, 5, 6, 7, 12])] for data in evaluation_data) # selecting 8 interesting stations\n",
        "eval_temps, _, eval_dates, _ = selected_evaluation_data\n",
        "for temp_seq, date_seq in zip(eval_temps, eval_dates):\n",
        "    prime_data = temp_seq[OFFSET:OFFSET+PRIMELEN]\n",
        "    results = keras_prediction_run(inference_model, prime_data, RUNLEN)\n",
        "    picture_this_6(temp_seq, date_seq, prime_data, results, PRIMELEN, RUNLEN, OFFSET, RMSELEN)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxJghm3afqsV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rmses = []\n",
        "bad_ones = 0\n",
        "eval_temps, _, eval_dates, _ = selected_evaluation_data # use evaluation_data for a more accurate eval\n",
        "for offset in [YEAR, YEAR+QYEAR, YEAR+2*QYEAR]:\n",
        "    for evaldata in eval_temps:\n",
        "        prime_data = evaldata[offset:offset+PRIMELEN]\n",
        "        results = keras_prediction_run(inference_model, prime_data, RUNLEN)\n",
        "        rmse = math.sqrt(np.mean((evaldata[offset+PRIMELEN:offset+PRIMELEN+RMSELEN] - results[:RMSELEN])**2))\n",
        "        rmses.append(rmse)\n",
        "        if rmse>5: bad_ones += 1\n",
        "        print(\"RMSE on {} predictions (shaded area): {}\".format(RMSELEN, rmse))\n",
        "print(\"Average RMSE on {} weather stations: {} ({} really bad ones, i.e. >5.0)\".format(len(evaltemps), np.mean(rmses), bad_ones))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fTrgLH7efqsX",
        "colab_type": "text"
      },
      "source": [
        "Copyright 2019 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    }
  ]
}